Running a list of functions with different params with a single command

Question

I am looking to test outcome of different regression/classification algorithms (i.e. svm, nnet, rpart, randomForest, naiveBayes, etc.) on the same data, to see which works better. But I need to have my code as short and clean as possible. To test all algorithms, I want to run them using a single `mclapply()` call of package `multicore`: ``` invisible(lapply(c("party","nnet","caret","klaR","randomForest","e1071","rpart", "multicore"), require, character.only = T)) algorithms <- c(knn3, NaiveBayes, nnet, ctree, randomForest, svm, naiveBayes, rpart) data(iris) model <- mclapply(algorithms, function(alg) alg(Species ~ ., iris)) ``` The problem is that some of the algorithms need extra parameters, i.e. `nnet()` needs parameter `size` to be set. For sure this can be fixed through several `if,else` commands, but is there any simpler solution?

Accepted Answer

One thing you could do is replace those in `algorithms` that require additional arguments with partial functions, e.g.

```
algorithms <- c(knn3, ctree, function(...) nnet(..., size=2))
```

Running a list of functions with different params with a single command

Solution

Problem